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Updated: Oct 4, 2025
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
miRNA-Based Signature to Predict the Development of Alzheimer's Disease
Fangfang Zhan1, Jinshan Yang2, Shifang Lin2
1Department of Neurology, The Affiliated Hospital of Putian University, Putian 351106, China.
Background:
Patients with mild cognitive impairment (MCI) suffer from a high risk of developing Alzheimer's disease (AD). Cumulative evidence has demonstrated that the development of AD is a complex process that could be modulated by miRNAs. Here, we aimed to identify miRNAs involved in the pathway, and interrogate their ability to predict prognosis in patients with MCI.
Methods:
We obtained the miRNA-seq profiles and the clinical characteristics of patients with MCI from the Gene Expression Omnibus (GEO). Cox regression analysis was used to construct a risk level model. The receiver operating characteristic (ROC) curve was used to assess the performance of the model for predicting prognosis. Combined with clinical characteristics, factors associated with prognosis were identified and a predictive prognosis nomogram was developed and validated. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we evaluated molecular signatures for the candidate miRNAs.
Results:
Our analysis identified 120 DEmiRNAs. The Cox regression analysis showed that two miRNAs could serve as risk factors for disease development. A risk level model was constructed. Age, apoe4, and risk level were associated with the prognosis. We developed a nomogram to predict disease progression. The calibration curve and concordance index (C-index) demonstrated the reliability of the nomogram. Functional enrichment analysis showed that these miRNAs were involved in regulating both cGMP-PKG and Sphingolipid signaling pathways.
Conclusion:
We have identified miRNAs associated with the development of MCI. These miRNAs could be used for early diagnosis and surveillance in patients with MCI, enabling prediction of the development of AD.
Insights
We identified specific microRNAs (miRNAs) linked to mild cognitive impairment (MCI) progression. These miRNAs can aid in early diagnosis and monitoring for Alzheimer's disease (AD) risk.
Area of Science:
- Biochemistry
- Genetics
- Neuroscience
Background:
- Mild cognitive impairment (MCI) patients have a high risk of developing Alzheimer's disease (AD).
- MicroRNAs (miRNAs) are increasingly recognized as modulators in AD pathogenesis.
- Identifying prognostic biomarkers for MCI is crucial for early intervention.
Purpose of the Study:
- To identify specific miRNAs involved in the progression of MCI.
- To evaluate the prognostic capability of these miRNAs in predicting Alzheimer's disease development.
- To develop a predictive model for MCI prognosis.
Main Methods:
- Utilized miRNA sequencing data and clinical information from MCI patients (GEO database).
- Employed Cox regression analysis to build a risk stratification model.
- Developed and validated a predictive nomogram incorporating clinical factors and miRNA risk levels.
- Performed Gene Ontology (GO) and KEGG pathway analyses for molecular insights.
Main Results:
- Identified 120 differentially expressed miRNAs (DEmiRNAs) in MCI.
- Two specific miRNAs were identified as significant risk factors for disease progression.
- A predictive model incorporating age, apoe4 status, and miRNA risk level demonstrated high reliability (C-index).
- Functional analysis revealed involvement in cGMP-PKG and Sphingolipid signaling pathways.
Conclusions:
- Identified key miRNAs associated with MCI progression and Alzheimer's disease risk.
- These miRNAs show potential as biomarkers for early diagnosis and surveillance in MCI patients.
- The developed nomogram provides a reliable tool for predicting MCI to AD transition.
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